Modeling and random search optimization for the polysilicon CVD reactor; Results in Control and Optimization; Vol. 13

Modeling and random search optimization for the polysilicon CVD reactor; Results in Control and Optimization; Vol. 13

Bibliografiske detaljer
Parent link:Results in Control and Optimization.— .— Amsterdam: Elsevier Science Publishing Company Inc.
Vol. 13.— 2023.— Article number 100320, 10 p.
Institution som forfatter: Томский политехнический университет (570)
Andre forfattere: Xi Bangwen, Xiong Gang, Kozin K. A. Kirill Andreevich, He Chang, Tamir T. S. Tariku Sinshaw, Song Yonggang, Liu Xiong, Shen Zhen
Summary:Title screen
Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.
Текстовый файл
AM_Agreement
Sprog:engelsk
Udgivet: 2023
Fag:
электронный ресурс
труды учёных ТПУ
CVD reactor
Simulator
Random search
Nonlinear regression fitting
Online adgang:https://doi.org/10.1016/j.rico.2023.100320
Format: Electronisk Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=673562

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